More information about the Canada-B.C. Water Quality Monitoring Program

Frequently asked questions about the Canada-B.C. Water Quality Monitoring Program.

 Program Overview


Purpose of the Federal-Provincial Water Quality Monitoring Network

The purpose of the Network is to deliver water quality information using consistent and comparable methods. We use this information to assess current water quality conditions, look for trends, identify emerging threats to aquatic life, and track results of management decisions.

We collect water samples every two weeks or every month from each station and analyse them for a range of water quality parameters. We define a set of core parameters for every station including metals, nitrogen, phosphorus, turbidity, specific conductance, hardness, suspended sediment, colour, organic carbon, calcium, magnesium, silicon, potassium and temperature. We also add other parameters if there is a specific issue in the watershed. 


Available water quality data from the network 

Download raw data from or from Environment and Climate Change Canada.

Environment and Climate Change Canada uses the data to support the national water quality indicator. It also supports reporting through various agreements between B.C and the governments of Yukon, Alberta and the Northwest Territories


Water Quality Indicators

Every year, Environment and Climate Change Canada reports water quality indicator scores and categories for 175 water quality stations across Canada. We compare water quality data from a single monitoring station to water quality guidelines and create a score for that station (ECCC, 2020). This score is called the water quality indicator and it provides a measure of the ability of river water to support plants and animals.  

Environment and Climate Change Canada calculates the indicator using the water quality index calculation (CCME, 2001). We compare concentration values over a three year period for 5 to 15 parameters to their guideline values at each station. An index score is calculated between 1 and 100 based on the selected parameters, and stations are assigned a category based on that score. The frequency and amplitude by which a specific parameter concentration exceeds its guideline value will drive the score down.  

We use concentration values from the most recent three years of data at a specific station to calculate the water quality index. The most recent indicator category and the years of sampling data that we used to calculate the score is displayed in the mapping tool.

Water quality stations are rated from poor to excellent. For example, water quality is considered excellent when a parameter never exceeds their guideline. Water quality is rated poor when parameters frequently exceed their guidelines, sometimes by a wide margin (ECCC, 2020; CCME, 2001).

  • Excellent: water quality is protected with a virtual absence of threat or impairment; conditions very close to natural or pristine levels.
  • Good: water quality is protected with only a minor degree of threat or impairment; conditions rarely depart from natural or desirable levels.
  • Fair: water quality is usually protected but occasionally threatened or impaired; conditions sometimes depart from natural or desirable levels.
  • Marginal: water quality is frequently threatened or impaired; conditions often depart from natural or desirable levels.
  • Poor: water quality is almost always threatened or impaired; conditions usually depart from natural or desirable levels.

Stations are selected to ensure that major basins in Canada are equally represented and that drainage areas are independent of one another. The indicator focuses on the regions in Canada where human activity is more prevalent as it is usually the main factor for water quality deterioration. The national report covers 16 of Canada’s 25 drainage basins (ECCC, 2020).


Water Quality Trends

Pollution from urban, industrial and agricultural areas poses a threat to water quality and aquatic life. We assessed data from the Network for trends to see if the water quality is changing over time.

We use publicly available data from Data with a status code of V (Validated) or P (Provisional) were included in the datasets. Replicate and blank samples collected for quality assurance/quality control were not included.

Water quality parameters characterize the physical and chemical conditions of a river or stream. We focused on analysing parameters with B.C. water quality guidelines for the protection of aquatic life. We also included parameters such as total phosphorus, nitrogen, hardness, and specific conductance.

  • Nutrients (6) – ammonia, nitrate, nitrate+nitrite, nitrite, total nitrogen and total phosphorus

  • Carbon (2) – dissolved organic carbon, total organic carbon

  • Major Ions & Other Inorganics (4) – chloride, fluoride, sulphate and hardness

  • General (7) – total alkalinity, dissolved oxygen, pH, non-filterable residue, specific conductance, turbidity and water temperature

  • Metals (4 dissolved, 20 total) – aluminum* , antimony, arsenic, barium, beryllium, boron, cadmium* , chromium, cobalt, copper* , iron* , lead, manganese, molybdenum, nickel, selenium, silver, thallium, uranium, zinc

 * indicates dissolved and total metals were included, otherwise only total were included

It is not possible for a laboratory to measure the concentration of a parameter in water to zero. Therefore, the laboratory defines a number for the lowest concentration that they can measure. Laboratories refer to this as a method detection or reporting limit. When the concentration of a parameter in the water is too low, the laboratory will report their minimum concentration with a less than sign indicating that the value is less than what they can measure. We used half the detection limit in the analysis and refer to this value as "value too low to detect."

Detection limits change as laboratories improve their instruments or analytical methods. This leads to multiple detection limits for a single parameter in the dataset. In this case, the “value too low to detect” will be half of the highest detection limit.

We calculated trends for a ten year period for all stations from January 2005 to December 2014. There were major laboratory changes in both 2003 and 2015. We need to see how this change may impact the data before we analyse other time periods.

After we collect a sample, it takes time to complete the laboratory analysis and validate the data. It also takes time to develop the study, analyse, and review the results before releasing them.

Some water quality data may not be suitable for trend analysis. We established a set of criteria to ensure that the data met a minimum standard. 

  1. The dataset must have data over the entire 10 year period beginning in 2005 and ending in 2014.
  2. Sometimes, samples don't get collected. The weather could be bad or the water levels are too high. To ensure that there is enough data, the first two years and the last two years of the trend period must have data in at least 9 of the 12 months. The remaining 6 middle years must have at least 60% of the data or 44 out of 72 months. 

  3. No more than 80% of the values are below the highest method detection limit in the entire 10-year dataset.

Parameters that did not meet this criteria are excluded from the reports. 

We report stations that were started after 2005 or that are not sampled monthly as ‘Insufficient data for trend analysis’.

This study involved analysing a large number of water quality parameters collected from many stations. We needed a method that was easy to use and that could be applied consistently to a wide variety of data from all stations. For this reason, we used non-parametric methods. Non-parametric methods are less affected by characteristics commonly in water quality data (such as non-normal distribution of the data, presence of outliers, values below detection limits, and missing values). 

We used the Seasonal Kendall test (Hirsch et al., 1982) to identify statistically significant trends, and the seasonal Sen slope estimate (Sen, 1968; Hirsch and Slack, 1984) to assess the magnitude of the trend. The Seasonal Kendall test is an extension of the Mann Kendall test for monotonic trends that accounts for the presence of seasonal patterns in the data by calculating the Mann Kendall test on each season and combining the results (Hirsch et al, 1982). We used this test because it does not make assumptions about the distribution of the data, it allows missing values and values below method detection limits without biasing the analysis (Hirsch et al., 1982).

Information about how we Calculate Water Quality Trends is available upon request.

Trend results are classified as increasing, decreasing, or "no evidence of trend". Categories are based on the statistical significance and direction of a detected trend:

  • Increasing Trend: the trend was significantly different from zero with an increasing trend.

  • No evidence of trend: there is not enough evidence to conclude if an increasing or decreasing trend is present.

  • Decreasing Trend: the trend was significantly different from zero with a decreasing trend.

A trend is classified as increasing or decreasing only when the Seasonal Kendall test for trend was found to be statistically significant (p-value < 0.1). When the trend was ‘not significant’ (p >= 0.1) it was classified as "no evidence of trend". This means that there is insufficient evidence to confidently determine if the trend is increasing or decreasing; it does not mean there is "no trend".

We do not report the slope estimate when more than 5% of the data is less than the detection limit.  In this case the graphs show "Not enough data to estimate trend size." Data was replaced with a value of one-half of the detection limit when a value is reported as below the detection limit. Therefore, the slope calculation is imprecise when there are too many values below detection limits in the dataset. For some parameters, there is more than one detection limit in the dataset. In this case, we replaced the value with half of the highest detection limit.

The Seasonal Kendall test is a non-parametric test based on ranks rather than actual values (i.e. assigned values are compared to determine which is larger). This test is therefore unaffected by the assigned value used to replace the concentration values below the detection limit. This is not the case for the Sen slope estimate which relies on values in the calculation and is dependent on the value selected to represent the values below detection limits (Helsel, 2012).

Since the calculations for significance and slope are based on these two separate steps, it is possible for the analysis to report a "significant trend" as increasing/decreasing but have a slope estimate of zero. This typically occurs when many of the assigned values are the same.

In short, because monitoring varies among stations. The Canada-B.C. monitoring program has a core set of variables it typically monitors for at each station. However, we monitor some stations for extra parameters based on specific water quality threats. The characteristics and completeness of the data also affect whether the data is included in the analysis. For example, a data record was required to have at least 20 percent of values above the method detection limit for trend analysis. The concentrations of some parameters in water are very low and do not meet this criteria. See ‘Criteria for including data in the analysis’ above for more details on how we screened the data.

The graphs display the concentrations for a single water quality parameter over time (2005-2014).

We collect water samples from the river and send them to a laboratory to be analysed for specific water quality parameters. The solid circles on the graph represent concentration values above the laboratory detection limit. The detection limit is the lowest concentration that the laboratory can report for a given parameter.  The value of an open circle on the graph is not the true concentration of a parameter in the water but half of the detection limit because the actual concentration is below what the laboratory can measure. We call this a "value too low to detect".  The estimated trend is shown as a straight orange line. The slope estimate may be unreliable if there are too many values in the dataset below the detection limit. We only show a trend line if the trend is statistically significant and the dataset has less than 5% of values below the highest detection limit. 

The trend box above the graph shows the direction and slope of the trend in concentration units per year. If the box says "no evidence of trend" this means that there is not enough evidence to determine if there is an increasing or decreasing trend; it does not mean that there is “no trend.” We estimate the slope of the trend using a separate step so it is possible for a trend to be increasing/decreasing and have a slope estimate of zero.  

In the detailed trend report, the B.C. guideline value for the protection of aquatic life is in the light grey box. B.C. developed guidelines to assess potential risks to water quality for many uses. A statistically significant trend may not be an environmental concern, so we show the guideline to protect aquatic life for context. This will help you determine if the trend presents a risk to fish or other aquatic life. We convert guideline values to match the units on the graph if they are different.


Environment and Climate Change Canada (202 0). Calculating water quality trends (available by request).

Canadian Council of Ministers of the Environment (2001) CCME Water Quality Index 1.0 User’s Manual. Retrieved on April 23, 2020. (PDF; 84.3 kB)

Environment and Climate Change Canada (ECCC), 2020. Canadian Environmental Sustainability Indicators: Water quality in Canadian Rivers. Retrieved on April 22, 2020. Available at:

Helsel, D. R. (2012). Statistics for censored environmental data using Minitab and R: . New York: John Wiley & Sons.

Hirsch, R. M., Slack, J. R., & Smith, R. A. (1982). Techniques of trend analysis for monthly water quality data. Water Resources Research, 18(1), 107-121.doi:10.1029/WR018i001p00107

Hirsch, R. M., & Slack, J. R. (1984). A Nonparametric Trend Test for Seasonal Data With Serial Dependence. Water Resources Research, 20(6), 727-732. doi:10.1029/WR020i006p00727

Sen, P. K. (1968). Estimates of the Regression Coefficient Based on Kendall’s Tau. Journal of the American Statistical Association, 63(324), 1379-1389. doi:10.1080/01621459.1968.10480934


Contact Information

Contact the water quality scientists at for inquiries about the data, study methods or program.